Jefferson Provost
- Cognitive Neuroscience top 1%
- Developmental and Educational Psychology top 1%
- Experimental and Cognitive Psychology top 1%
- Social Psychology top 5%
- Artificial Intelligence top 5%
- Co-authors
- Brian MacWhinneyJonathan CohenMatthew FlattBenjamin KuipersRisto MiikkulainenPatrick BeesonJoseph ModayilYoonsuck Choe
- Topics
- Robot Manipulation and Learning (2 papers)Reinforcement Learning in Robotics (2 papers)Modular Robots and Swarm Intelligence (1 paper)
- Cited by
- Cognitive NeuroscienceDevelopmental and Educational PsychologyExperimental and Cognitive Psychology
- Journals
- NeurocomputingConnection ScienceSpatial Vision
- Partner nations
- United States
In The Last Decade
Jefferson Provost
7 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 114
- Cognitive Neuroscience 2.0k
- Developmental and Educational Psychology 986
- Experimental and Cognitive Psychology 929
- Social Psychology 333
- Artificial Intelligence 243
Countries citing papers authored by Jefferson Provost
This map shows the geographic impact of Jefferson Provost's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jefferson Provost with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jefferson Provost more than expected).
Fields of papers citing papers by Jefferson Provost
This network shows the impact of papers produced by Jefferson Provost. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jefferson Provost. The network helps show where Jefferson Provost may publish in the future.
Co-authorship network of co-authors of Jefferson Provost
This figure shows the co-authorship network connecting the top 25 collaborators of Jefferson Provost. A scholar is included among the top collaborators of Jefferson Provost based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jefferson Provost. Jefferson Provost is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 31 | |
| 3 | Self-Organizing Perceptual and Temporal Abstraction for Robot Reinforcement Learning | 10 |
| 4 | 9 | |
| 5 | Toward Learning the Causal Layer of the Spatial Semantic Hierarchy using SOMs | 2 |
| 6 | 97 | |
| 7 | PsyScope: An interactive graphic system for designing and controlling experiments in the psychology laboratory using Macintosh computersbreakdown → | 2555 |
About Jefferson Provost
Jefferson Provost is a scholar working on Signal Processing, Geography, Planning and Development and Biophysics, having authored 7 papers that have together received 2.7k indexed citations. Recurring topics across this work include Robot Manipulation and Learning (2 papers), Reinforcement Learning in Robotics (2 papers) and Modular Robots and Swarm Intelligence (1 paper). The work is most often cited by research in Cognitive Neuroscience (2.0k citations), Developmental and Educational Psychology (986 citations) and Experimental and Cognitive Psychology (929 citations). Jefferson Provost has collaborated with scholars based in United States. Frequent co-authors include Brian MacWhinney, Jonathan Cohen, Matthew Flatt, Jonathan Cohen, Benjamin Kuipers, Risto Miikkulainen, Patrick Beeson, Joseph Modayil, Risto Miikkulainen and Yoonsuck Choe. Their work appears in journals such as Neurocomputing, Connection Science and Spatial Vision.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.